Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations17704768
Missing cells39598175
Missing cells (%)18.6%
Duplicate rows39894
Duplicate rows (%)0.2%
Total size in memory1.7 GiB
Average record size in memory104.0 B

Variable types

Numeric8
DateTime1
Categorical2
Text1

Alerts

Dataset has 39894 (0.2%) duplicate rowsDuplicates
ECGHR is highly overall correlated with SPO2HRHigh correlation
NIBP_lower is highly overall correlated with NIBP_mean and 1 other fieldsHigh correlation
NIBP_mean is highly overall correlated with NIBP_lower and 1 other fieldsHigh correlation
NIBP_upper is highly overall correlated with NIBP_lower and 1 other fieldsHigh correlation
SPO2HR is highly overall correlated with ECGHRHigh correlation
location is highly overall correlated with monitor_idHigh correlation
monitor_id is highly overall correlated with locationHigh correlation
ECGHR has 2290539 (12.9%) missing values Missing
ECGRR has 2010733 (11.4%) missing values Missing
SPO2HR has 6346490 (35.8%) missing values Missing
SPO2 has 6253817 (35.3%) missing values Missing
PI has 6614915 (37.4%) missing values Missing
NIBP_lower has 4736804 (26.8%) missing values Missing
NIBP_upper has 4766643 (26.9%) missing values Missing
NIBP_mean has 4728537 (26.7%) missing values Missing
datetime has 937436 (5.3%) missing values Missing
patient_id has 912261 (5.2%) missing values Missing
SPO2 is highly skewed (γ1 = -37.63812702) Skewed
NIBP_lower is highly skewed (γ1 = -23.48518115) Skewed
NIBP_mean is highly skewed (γ1 = -21.5716216) Skewed

Reproduction

Analysis started2024-11-02 15:13:32.926682
Analysis finished2024-11-02 15:27:19.345861
Duration13 minutes and 46.42 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ECGHR
Real number (ℝ)

High correlation  Missing 

Distinct347
Distinct (%)< 0.1%
Missing2290539
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean136.03064
Minimum-999
Maximum357
Zeros31
Zeros (%)< 0.1%
Negative233
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:19.475444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile97
Q1120
median135
Q3151
95-th percentile177
Maximum357
Range1356
Interquartile range (IQR)31

Descriptive statistics

Standard deviation25.886748
Coefficient of variation (CV)0.19030086
Kurtosis58.221672
Mean136.03064
Median Absolute Deviation (MAD)15
Skewness-0.90921981
Sum2.0968074 × 109
Variance670.12372
MonotonicityNot monotonic
2024-11-02T15:27:19.717155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 286437
 
1.6%
131 277478
 
1.6%
142 276789
 
1.6%
126 275693
 
1.6%
140 275575
 
1.6%
138 275038
 
1.6%
139 274175
 
1.5%
130 272722
 
1.5%
137 271423
 
1.5%
129 267484
 
1.5%
Other values (337) 12661415
71.5%
(Missing) 2290539
 
12.9%
ValueCountFrequency (%)
-999 233
 
< 0.1%
0 31
 
< 0.1%
13 69
 
< 0.1%
14 77
 
< 0.1%
15 1842
< 0.1%
16 509
 
< 0.1%
17 937
< 0.1%
18 389
 
< 0.1%
19 962
< 0.1%
20 482
 
< 0.1%
ValueCountFrequency (%)
357 2
 
< 0.1%
356 2
 
< 0.1%
355 3
 
< 0.1%
354 2
 
< 0.1%
353 3
 
< 0.1%
352 4
 
< 0.1%
351 4
 
< 0.1%
350 13
< 0.1%
349 2
 
< 0.1%
348 1
 
< 0.1%

ECGRR
Real number (ℝ)

Missing 

Distinct201
Distinct (%)< 0.1%
Missing2010733
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean40.795339
Minimum-999
Maximum200
Zeros116604
Zeros (%)0.7%
Negative96
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:19.945676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile22
Q131
median39
Q349
95-th percentile68
Maximum200
Range1199
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.763162
Coefficient of variation (CV)0.36188355
Kurtosis152.60339
Mean40.795339
Median Absolute Deviation (MAD)9
Skewness-1.335996
Sum6.4024347 × 108
Variance217.95095
MonotonicityNot monotonic
2024-11-02T15:27:20.183648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 499595
 
2.8%
33 499167
 
2.8%
35 497812
 
2.8%
37 488002
 
2.8%
32 485866
 
2.7%
36 484739
 
2.7%
38 483823
 
2.7%
39 474982
 
2.7%
40 464046
 
2.6%
31 462501
 
2.6%
Other values (191) 10853502
61.3%
(Missing) 2010733
 
11.4%
ValueCountFrequency (%)
-999 96
 
< 0.1%
0 116604
0.7%
2 403
 
< 0.1%
3 509
 
< 0.1%
4 1529
 
< 0.1%
5 3053
 
< 0.1%
6 2619
 
< 0.1%
7 2684
 
< 0.1%
8 3877
 
< 0.1%
9 4987
 
< 0.1%
ValueCountFrequency (%)
200 374
< 0.1%
199 4
 
< 0.1%
198 7
 
< 0.1%
197 1
 
< 0.1%
196 11
 
< 0.1%
195 10
 
< 0.1%
194 47
 
< 0.1%
193 8
 
< 0.1%
192 9
 
< 0.1%
191 6
 
< 0.1%

SPO2HR
Real number (ℝ)

High correlation  Missing 

Distinct287
Distinct (%)< 0.1%
Missing6346490
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean129.91543
Minimum-999
Maximum300
Zeros0
Zeros (%)0.0%
Negative3701
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:20.409659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile92
Q1116
median131
Q3145
95-th percentile168
Maximum300
Range1299
Interquartile range (IQR)29

Descriptive statistics

Standard deviation31.183333
Coefficient of variation (CV)0.24002794
Kurtosis558.18151
Mean129.91543
Median Absolute Deviation (MAD)14
Skewness-15.590981
Sum1.4756156 × 109
Variance972.40025
MonotonicityNot monotonic
2024-11-02T15:27:20.653358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 221046
 
1.2%
128 217411
 
1.2%
130 216650
 
1.2%
129 214960
 
1.2%
127 214436
 
1.2%
126 209768
 
1.2%
132 209526
 
1.2%
122 208236
 
1.2%
136 207661
 
1.2%
123 206993
 
1.2%
Other values (277) 9231591
52.1%
(Missing) 6346490
35.8%
ValueCountFrequency (%)
-999 3701
< 0.1%
10 2
 
< 0.1%
15 6
 
< 0.1%
16 109
 
< 0.1%
17 380
 
< 0.1%
18 412
 
< 0.1%
19 365
 
< 0.1%
20 441
 
< 0.1%
21 494
 
< 0.1%
22 528
 
< 0.1%
ValueCountFrequency (%)
300 69
< 0.1%
299 19
 
< 0.1%
298 9
 
< 0.1%
297 10
 
< 0.1%
296 6
 
< 0.1%
295 11
 
< 0.1%
294 9
 
< 0.1%
293 26
 
< 0.1%
292 14
 
< 0.1%
291 23
 
< 0.1%

SPO2
Real number (ℝ)

Missing  Skewed 

Distinct102
Distinct (%)< 0.1%
Missing6253817
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean92.700092
Minimum-999
Maximum100
Zeros47524
Zeros (%)0.3%
Negative3648
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:20.881117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile78
Q192
median96
Q398
95-th percentile100
Maximum100
Range1099
Interquartile range (IQR)6

Descriptive statistics

Standard deviation22.363546
Coefficient of variation (CV)0.24124621
Kurtosis1808.1425
Mean92.700092
Median Absolute Deviation (MAD)3
Skewness-37.638127
Sum1.0615042 × 109
Variance500.12819
MonotonicityNot monotonic
2024-11-02T15:27:21.107974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1524471
 
8.6%
98 1273409
 
7.2%
100 1246011
 
7.0%
97 1128314
 
6.4%
96 981395
 
5.5%
95 836675
 
4.7%
94 672799
 
3.8%
93 576270
 
3.3%
92 467984
 
2.6%
91 416756
 
2.4%
Other values (92) 2326867
 
13.1%
(Missing) 6253817
35.3%
ValueCountFrequency (%)
-999 3648
 
< 0.1%
0 47524
0.3%
1 591
 
< 0.1%
2 240
 
< 0.1%
3 1257
 
< 0.1%
4 1305
 
< 0.1%
5 1102
 
< 0.1%
6 1085
 
< 0.1%
7 1282
 
< 0.1%
8 732
 
< 0.1%
ValueCountFrequency (%)
100 1246011
7.0%
99 1524471
8.6%
98 1273409
7.2%
97 1128314
6.4%
96 981395
5.5%
95 836675
4.7%
94 672799
3.8%
93 576270
 
3.3%
92 467984
 
2.6%
91 416756
 
2.4%

PI
Real number (ℝ)

Missing 

Distinct3709
Distinct (%)< 0.1%
Missing6614915
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean3.0218485
Minimum-9.99
Maximum20
Zeros0
Zeros (%)0.0%
Negative3334
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:21.325225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.99
5-th percentile0.25
Q10.9501953
median1.9003906
Q33.5390625
95-th percentile9.2734375
Maximum20
Range29.99
Interquartile range (IQR)2.5888672

Descriptive statistics

Standard deviation3.7726961
Coefficient of variation (CV)1.2484729
Kurtosis10.523714
Mean3.0218485
Median Absolute Deviation (MAD)1.1503906
Skewness3.0715947
Sum33511856
Variance14.233236
MonotonicityNot monotonic
2024-11-02T15:27:21.552438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 315674
 
1.8%
0.049987793 33838
 
0.2%
0.5800781 33549
 
0.2%
0.05999756 33429
 
0.2%
0.6699219 33347
 
0.2%
0.75 33302
 
0.2%
0.6201172 33183
 
0.2%
0.5600586 33140
 
0.2%
0.60009766 33139
 
0.2%
0.64990234 33128
 
0.2%
Other values (3699) 10474124
59.2%
(Missing) 6614915
37.4%
ValueCountFrequency (%)
-9.99 3334
 
< 0.1%
0.010002136 19
 
< 0.1%
0.02 1
 
< 0.1%
0.020004272 72
 
< 0.1%
0.02999878 2990
 
< 0.1%
0.03 80
 
< 0.1%
0.04 763
 
< 0.1%
0.040008545 23635
0.1%
0.049987793 33838
0.2%
0.05 983
 
< 0.1%
ValueCountFrequency (%)
20 315674
1.8%
19.99 3
 
< 0.1%
19.984375 145
 
< 0.1%
19.98 1
 
< 0.1%
19.97 4
 
< 0.1%
19.96875 71
 
< 0.1%
19.96 1
 
< 0.1%
19.953125 159
 
< 0.1%
19.95 1
 
< 0.1%
19.94 3
 
< 0.1%

NIBP_lower
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct166
Distinct (%)< 0.1%
Missing4736804
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean60.837779
Minimum-999
Maximum177
Zeros0
Zeros (%)0.0%
Negative8408
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:21.767442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile39
Q151
median60
Q370
95-th percentile91
Maximum177
Range1176
Interquartile range (IQR)19

Descriptive statistics

Standard deviation31.937795
Coefficient of variation (CV)0.52496648
Kurtosis783.85212
Mean60.837779
Median Absolute Deviation (MAD)10
Skewness-23.485181
Sum7.8894213 × 108
Variance1020.0227
MonotonicityNot monotonic
2024-11-02T15:27:22.148688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 480200
 
2.7%
63 404660
 
2.3%
61 390312
 
2.2%
58 384555
 
2.2%
57 382199
 
2.2%
54 380048
 
2.1%
51 358793
 
2.0%
55 352933
 
2.0%
56 339251
 
1.9%
64 335988
 
1.9%
Other values (156) 9159025
51.7%
(Missing) 4736804
26.8%
ValueCountFrequency (%)
-999 8408
< 0.1%
4 2
 
< 0.1%
6 90
 
< 0.1%
7 339
 
< 0.1%
9 369
 
< 0.1%
10 668
 
< 0.1%
11 858
 
< 0.1%
12 4265
< 0.1%
13 1288
 
< 0.1%
14 1497
 
< 0.1%
ValueCountFrequency (%)
177 6943
< 0.1%
175 83
 
< 0.1%
172 172
 
< 0.1%
171 1
 
< 0.1%
170 835
 
< 0.1%
169 302
 
< 0.1%
168 90
 
< 0.1%
166 359
 
< 0.1%
165 276
 
< 0.1%
164 368
 
< 0.1%

NIBP_upper
Real number (ℝ)

High correlation  Missing 

Distinct209
Distinct (%)< 0.1%
Missing4766643
Missing (%)26.9%
Infinite0
Infinite (%)0.0%
Mean111.78728
Minimum-999
Maximum233
Zeros0
Zeros (%)0.0%
Negative8408
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:22.381360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile80
Q199
median110
Q3123
95-th percentile153
Maximum233
Range1232
Interquartile range (IQR)24

Descriptive statistics

Standard deviation36.422343
Coefficient of variation (CV)0.32581832
Kurtosis560.11908
Mean111.78728
Median Absolute Deviation (MAD)12
Skewness-18.162016
Sum1.4463178 × 109
Variance1326.5871
MonotonicityNot monotonic
2024-11-02T15:27:22.615544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 382044
 
2.2%
105 335290
 
1.9%
113 325889
 
1.8%
104 323265
 
1.8%
110 316999
 
1.8%
107 313527
 
1.8%
111 312695
 
1.8%
109 301632
 
1.7%
112 295437
 
1.7%
101 295163
 
1.7%
Other values (199) 9736184
55.0%
(Missing) 4766643
26.9%
ValueCountFrequency (%)
-999 8408
< 0.1%
25 1001
 
< 0.1%
26 215
 
< 0.1%
27 449
 
< 0.1%
28 10
 
< 0.1%
29 10
 
< 0.1%
30 23
 
< 0.1%
32 339
 
< 0.1%
33 712
 
< 0.1%
34 88
 
< 0.1%
ValueCountFrequency (%)
233 353
 
< 0.1%
232 195
 
< 0.1%
231 52
 
< 0.1%
230 99
 
< 0.1%
229 395
 
< 0.1%
228 251
 
< 0.1%
227 305
 
< 0.1%
226 693
 
< 0.1%
225 5519
< 0.1%
224 1050
 
< 0.1%

NIBP_mean
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct183
Distinct (%)< 0.1%
Missing4728537
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean75.968258
Minimum-999
Maximum199
Zeros0
Zeros (%)0.0%
Negative8408
Negative (%)< 0.1%
Memory size270.2 MiB
2024-11-02T15:27:22.843805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile52
Q165
median74
Q385
95-th percentile110
Maximum199
Range1198
Interquartile range (IQR)20

Descriptive statistics

Standard deviation33.276121
Coefficient of variation (CV)0.43802664
Kurtosis703.50087
Mean75.968258
Median Absolute Deviation (MAD)10
Skewness-21.571622
Sum9.8578166 × 108
Variance1107.3002
MonotonicityNot monotonic
2024-11-02T15:27:23.080970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 495820
 
2.8%
74 417968
 
2.4%
69 375832
 
2.1%
65 368736
 
2.1%
63 364763
 
2.1%
72 353696
 
2.0%
73 349841
 
2.0%
71 341929
 
1.9%
77 341702
 
1.9%
68 338250
 
1.9%
Other values (173) 9227694
52.1%
(Missing) 4728537
26.7%
ValueCountFrequency (%)
-999 8408
< 0.1%
6 2
 
< 0.1%
9 339
 
< 0.1%
12 449
 
< 0.1%
13 10
 
< 0.1%
14 1834
 
< 0.1%
15 49
 
< 0.1%
16 1210
 
< 0.1%
17 352
 
< 0.1%
18 633
 
< 0.1%
ValueCountFrequency (%)
199 83
 
< 0.1%
198 416
 
< 0.1%
197 315
 
< 0.1%
191 181
 
< 0.1%
190 618
 
< 0.1%
189 395
 
< 0.1%
187 690
 
< 0.1%
186 443
 
< 0.1%
185 12490
0.1%
184 189
 
< 0.1%

datetime
Date

Missing 

Distinct7557712
Distinct (%)45.1%
Missing937436
Missing (%)5.3%
Memory size270.2 MiB
Minimum2022-07-01 02:00:00
Maximum2023-06-14 10:01:32
2024-11-02T15:27:23.294119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:27:23.518888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

monitor_id
Categorical

High correlation 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.2 MiB
IMPALA22050004
1253088 
IMPALA22060028
 
1042725
IMPALA22050003
 
987151
IMPALA22050005
 
969728
IMPALA22060033
 
924577
Other values (31)
12527499 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters247866752
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMPALA22050002
2nd rowIMPALA22050002
3rd rowIMPALA22050002
4th rowIMPALA22050002
5th rowIMPALA22050002

Common Values

ValueCountFrequency (%)
IMPALA22050004 1253088
 
7.1%
IMPALA22060028 1042725
 
5.9%
IMPALA22050003 987151
 
5.6%
IMPALA22050005 969728
 
5.5%
IMPALA22060033 924577
 
5.2%
IMPALA22060020 886970
 
5.0%
IMPALA22060035 869811
 
4.9%
IMPALA22060001 854888
 
4.8%
IMPALA22060026 834526
 
4.7%
IMPALA22060043 746685
 
4.2%
Other values (26) 8334619
47.1%

Length

2024-11-02T15:27:23.721133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
impala22050004 1253088
 
7.1%
impala22060028 1042725
 
5.9%
impala22050003 987151
 
5.6%
impala22050005 969728
 
5.5%
impala22060033 924577
 
5.2%
impala22060020 886970
 
5.0%
impala22060035 869811
 
4.9%
impala22060001 854888
 
4.8%
impala22060026 834526
 
4.7%
impala22060043 746685
 
4.2%
Other values (26) 8334619
47.1%

Most occurring characters

ValueCountFrequency (%)
0 60668347
24.5%
2 39567970
16.0%
A 35407608
14.3%
M 17705732
 
7.1%
I 17703804
 
7.1%
P 17703804
 
7.1%
L 17703804
 
7.1%
6 15900086
 
6.4%
4 7398320
 
3.0%
3 6128801
 
2.5%
Other values (9) 11978476
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 247866752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 60668347
24.5%
2 39567970
16.0%
A 35407608
14.3%
M 17705732
 
7.1%
I 17703804
 
7.1%
P 17703804
 
7.1%
L 17703804
 
7.1%
6 15900086
 
6.4%
4 7398320
 
3.0%
3 6128801
 
2.5%
Other values (9) 11978476
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 247866752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 60668347
24.5%
2 39567970
16.0%
A 35407608
14.3%
M 17705732
 
7.1%
I 17703804
 
7.1%
P 17703804
 
7.1%
L 17703804
 
7.1%
6 15900086
 
6.4%
4 7398320
 
3.0%
3 6128801
 
2.5%
Other values (9) 11978476
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 247866752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 60668347
24.5%
2 39567970
16.0%
A 35407608
14.3%
M 17705732
 
7.1%
I 17703804
 
7.1%
P 17703804
 
7.1%
L 17703804
 
7.1%
6 15900086
 
6.4%
4 7398320
 
3.0%
3 6128801
 
2.5%
Other values (9) 11978476
 
4.8%

patient_id
Text

Missing 

Distinct772
Distinct (%)< 0.1%
Missing912261
Missing (%)5.2%
Memory size270.2 MiB
2024-11-02T15:27:24.025534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters184717577
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB-S-0479386
2nd rowB-S-0479386
3rd rowB-S-0479386
4th rowB-S-0479386
5th rowB-S-0479386
ValueCountFrequency (%)
b-s-3584588 192408
 
1.1%
b-n-6628544 185262
 
1.1%
b-n-5518534 152082
 
0.9%
z-h-6309725 146497
 
0.9%
z-h-6984306 134473
 
0.8%
b-n-8176705 129457
 
0.8%
z-h-8676503 128954
 
0.8%
z-h-2903871 128006
 
0.8%
b-s-7748628 127742
 
0.8%
b-n-7429295 118020
 
0.7%
Other values (762) 15349606
91.4%
2024-11-02T15:27:24.609211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 33585014
18.2%
8 13502694
 
7.3%
4 12413424
 
6.7%
0 12159313
 
6.6%
6 11806320
 
6.4%
5 11797981
 
6.4%
7 11794785
 
6.4%
2 11713403
 
6.3%
3 11562685
 
6.3%
9 10819227
 
5.9%
Other values (6) 43562731
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 184717577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 33585014
18.2%
8 13502694
 
7.3%
4 12413424
 
6.7%
0 12159313
 
6.6%
6 11806320
 
6.4%
5 11797981
 
6.4%
7 11794785
 
6.4%
2 11713403
 
6.3%
3 11562685
 
6.3%
9 10819227
 
5.9%
Other values (6) 43562731
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 184717577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 33585014
18.2%
8 13502694
 
7.3%
4 12413424
 
6.7%
0 12159313
 
6.6%
6 11806320
 
6.4%
5 11797981
 
6.4%
7 11794785
 
6.4%
2 11713403
 
6.3%
3 11562685
 
6.3%
9 10819227
 
5.9%
Other values (6) 43562731
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 184717577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 33585014
18.2%
8 13502694
 
7.3%
4 12413424
 
6.7%
0 12159313
 
6.6%
6 11806320
 
6.4%
5 11797981
 
6.4%
7 11794785
 
6.4%
2 11713403
 
6.3%
3 11562685
 
6.3%
9 10819227
 
5.9%
Other values (6) 43562731
23.6%

location
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.2 MiB
zomba
9722905 
blantyre
7981863 

Length

Max length8
Median length5
Mean length6.3524938
Min length5

Characters and Unicode

Total characters112469429
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblantyre
2nd rowblantyre
3rd rowblantyre
4th rowblantyre
5th rowblantyre

Common Values

ValueCountFrequency (%)
zomba 9722905
54.9%
blantyre 7981863
45.1%

Length

2024-11-02T15:27:24.852160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T15:27:25.038062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
zomba 9722905
54.9%
blantyre 7981863
45.1%

Most occurring characters

ValueCountFrequency (%)
b 17704768
15.7%
a 17704768
15.7%
z 9722905
8.6%
o 9722905
8.6%
m 9722905
8.6%
l 7981863
7.1%
n 7981863
7.1%
t 7981863
7.1%
y 7981863
7.1%
r 7981863
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112469429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 17704768
15.7%
a 17704768
15.7%
z 9722905
8.6%
o 9722905
8.6%
m 9722905
8.6%
l 7981863
7.1%
n 7981863
7.1%
t 7981863
7.1%
y 7981863
7.1%
r 7981863
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112469429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 17704768
15.7%
a 17704768
15.7%
z 9722905
8.6%
o 9722905
8.6%
m 9722905
8.6%
l 7981863
7.1%
n 7981863
7.1%
t 7981863
7.1%
y 7981863
7.1%
r 7981863
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112469429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 17704768
15.7%
a 17704768
15.7%
z 9722905
8.6%
o 9722905
8.6%
m 9722905
8.6%
l 7981863
7.1%
n 7981863
7.1%
t 7981863
7.1%
y 7981863
7.1%
r 7981863
7.1%

Interactions

2024-11-02T15:25:11.215143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:22:47.171878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:09.582338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:30.558946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:49.164431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:07.721400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:27.301761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:49.128006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:13.875954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:22:50.865012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:12.745905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:33.057132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:51.638927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:10.254279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:30.035079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:52.297027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:15.965333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:22:53.341296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:15.262258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:35.501754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:54.283335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:12.693013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:32.287778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:54.805825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:18.085555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:22:55.885058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:17.801047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:38.029424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:56.677949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:15.302408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:34.456991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:57.123786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:20.168242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:22:58.292226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:20.102373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:40.365140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:58.975257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:17.743449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:36.643291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:59.469019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:23.225522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:01.018414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:22.693258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:42.451891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:01.046884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:19.964997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:39.713364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:02.636098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:26.252134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:03.713918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:25.416137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:44.578233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:03.230415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:22.180622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:43.023940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:05.517154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:29.123414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:06.359369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:28.119765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:23:46.724736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:05.282007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:24.489972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:24:46.123810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:25:08.515307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-02T15:27:25.164200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ECGHRECGRRNIBP_lowerNIBP_meanNIBP_upperPISPO2SPO2HRlocationmonitor_id
ECGHR1.0000.3240.0360.0420.035-0.121-0.1000.8570.0240.102
ECGRR0.3241.0000.0060.0080.006-0.097-0.1570.3190.0040.049
NIBP_lower0.0360.0061.0000.9100.716-0.0550.0070.0450.0940.163
NIBP_mean0.0420.0080.9101.0000.827-0.035-0.0040.0530.0540.166
NIBP_upper0.0350.0060.7160.8271.000-0.021-0.0120.0650.0700.182
PI-0.121-0.097-0.055-0.035-0.0211.0000.047-0.1510.0620.083
SPO2-0.100-0.1570.007-0.004-0.0120.0471.000-0.0640.0130.054
SPO2HR0.8570.3190.0450.0530.065-0.151-0.0641.0000.0250.080
location0.0240.0040.0940.0540.0700.0620.0130.0251.0001.000
monitor_id0.1020.0490.1630.1660.1820.0830.0540.0801.0001.000

Missing values

2024-11-02T15:25:34.059929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-02T15:25:53.886293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-02T15:26:41.502544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation
230858NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234325143.046.0NaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234324135.050.0NaN96.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234323139.048.0143.096.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234322144.044.0143.096.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234321145.041.0144.095.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234320107.025.0NaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234319104.026.0NaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234318103.026.0NaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
234317148.051.0145.094.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation
17613402NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060047NaNzomba
17613405NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060047NaNzomba
17615360NaN38.0NaNNaNNaNNaNNaNNaNNaNIMPALA22060047NaNzomba
17615362NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060047NaNzomba
17632620NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba
17632621NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba
17632622NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba
17632623NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba
17632657NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba
17632658NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060044NaNzomba

Duplicate rows

Most frequently occurring

ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation# duplicates
39854NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22050003NaNzomba65760
39863NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060014NaNzomba61399
39890NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060048NaNblantyre53210
39865NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060020NaNzomba29844
39873NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060033NaNblantyre22631
39862NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060013NaNzomba21272
39885NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060043NaNblantyre15694
39855NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22050004NaNblantyre13759
39889NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060047NaNzomba10048
39859NaNNaNNaNNaNNaNNaNNaNNaNNaNIMPALA22060002NaNblantyre8992